Titlebook: Genetic Programming; 4th European Confere Julian Miller,Marco Tomassini,William B. Langdon Conference proceedings 2001 Springer-Verlag Berl

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Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming migration between subpopulations: the number of individuals sent and the frequency of exchange. Our results suggest that fitness evolution is more sensitive to the migration factor than the communication topology.

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Evolving Turing Machines for Biosequence Recognition and Analysisolved Turing machines are capable of recognizing HIV biosequences in a collection of training sets. In addition, we demostrate that the evolved Turing machines can be used to approximate the multiple sequence alignment problem.

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Neutrality and the Evolvability of Boolean Function Landscapemental results indicate that there is a positive relationship between neutrality and evolvability: .. We also identify four characteristics of adaptive/neutral mutations that are associated with high evolvability. They may be the ingredients in designing effective Evolutionary Computation systems for the Boolean class problem.

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LAVA

Halfhearted

https://doi.org/10.1007/978-3-476-99935-1f how average size changes on flat landscapeswith glitches. The latter implies the surprising result that a single program glitch in an otherwise flat fitness landscape is sufficient to drive the average program size of an infinite population, which may have important implications for the control of code growth.

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Osteons

Doppel- und Neu-Ausfertigungen,here for the first time. In the paper we provide examples which show how the theory can be specialised to specific crossover operators and how it can be used to derive an exact definition of effective fitness and a size-evolution equation for GP.

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Heuristic Learning Based on Genetic Programmingalgorithm works best, i.e. large problem instances where standard evolutionary techniques cannot be applied due to their large runtimes. Our experiments show that we obtain high quality results that outperform previous methods, while keeping the advantage of low runtimes.

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A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representationsf how average size changes on flat landscapeswith glitches. The latter implies the surprising result that a single program glitch in an otherwise flat fitness landscape is sufficient to drive the average program size of an infinite population, which may have important implications for the control of code growth.